Background. Melanomas, the most common human malignancy, are primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy, and histopathological examination. We aimed to systematically review the performance and quality of machine learning-based methods in distinguishing melanoma and benign nevus in the relevant literature. Method. Four databases (Web of Science, PubMed, Embase, and the Cochrane library) were searched to retrieve the relevant studies published until March 26, 2022. The Predictive model Deviation Risk Assessment tool (PROBAST) was used to assess the deviation risk of opposing law. Result. This systematic review included thirty researches with 114007 subjects and 71 machine learning models. The convolutional neural network was the main machine learning method. The pooled sensitivity was 85% (95% CI 82–87%), the specificity was 86% (82–88%), and the C -index was 0.87 (0.84–0.90). Conclusion. The findings of our study showed that ML algorithms had high sensitivity and specificity for distinguishing between melanoma and benign nevi. This suggests that state-of-the-art ML-based algorithms for distinguishing melanoma from benign nevi may be ready for clinical use. However, a large proportion of the earlier published studies had methodological flaws, such as lack of external validation and lack of clinician comparisons. The results of these studies should be interpreted with caution.
Background: Trunk melanoma is one of the most common and deadly types of melanomas. Multiple factors are associated with the prognosis of patients with trunk melanoma. Currently, direct, and reliable clinical tools for early assessment of individual specific risk of death are limited, and most of them are prediction models for all-cause death. Their accuracy in predicting competitiveness events, which make up a relatively large portion, may be substantially compromised. Hence, we conducted this study to investigate the risk factors of trunk melanoma-specific death to establish a comprehensive prediction model suitable for clinical application.Methods: Patients with trunk melanoma analyzed in this study were from the SEER program [2010][2011][2012][2013][2014][2015].The random sampling method was used to split the included cases into the training and validation cohorts at a ratio of 7:3. Univariate and multivariate competing risk models were used to screen the independent influencing factors of specific death, and then a nomogram covering these independent predictors was constructed. The concordance index (C-index) and a calibration curve were used to evaluate the calibration degree and accuracy of the nomogram. Results:We identified 21,198 patients with trunk melanoma from the SEER database, and 3,814 of them died (17.99%). Among the death cases, deaths from other causes accounted for 66.50%The prognostic nomogram included 8 variables and 16 independent influencing factors. The overall C-index in the training set was 0.89, and the receiver operating characteristic (ROC) curve for predicting 1-, 3-, and 5-year survival was 0.928 [95% confidence interval (CI): 0.911-0.945], 0.907 (95% CI: 0.895-0.918), and 0.891 (95% CI: 0.879-0.902), respectively. The C-index of the model in the validation set was 0.89, and the area under the ROC curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific death (CSD) was 0.927 (95% CI: 0.899-0.955), 0.916 (95% CI: 0.901-0.930), and 0.905 (95% CI: 0.899-0.921). Both the training set and the validation set showed the ideal calibration degree.Conclusions: This model can be used as a potential tool for prognostic risk management of trunk melanoma in the presence of many competing events.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.